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quantitative proteomic data with a degree of statistical rigour using common freely available software packages; proficiency in handling large datasets including bioinformatics and biostatistics; and knowledge
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Institute of Particle Astrophysics and Cosmology (BIPAC), on research aimed at extracting cosmological information from large-scale structure (LSS) and Cosmic Microwave Background (CMB) probes on very large
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to define and evaluate AI biomarker models. You should hold a relevant technical PhD/DPhil (or near completion) in medical video analysis or computer vision with a video focus and ideally have prior
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bioinformatics and spatial analysis techniques to large-scale spatial transcriptomics and imaging datasets, using tools such as MuSpAn to identify spatial biomarkers and uncover the biological mechanisms driving
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(Large Language Models, Convolutional Neural Networks, Machine Learning) for analysis and classification of data.
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. Experience analysing large biological datasets, including RNA sequencing, metabolomics, proteomics or whole-genome sequencing data, is essential, along with strong quantitative and computational skills
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/opportunity-spaces/resilient-climate-and-ecosystems/accelerated-adaptation for more information on ARIA and this programme). The project is a collaboration between groups at the Universities of York and Exeter
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articles and present papers and posters. About You You will hold, or be close to completion of, a PhD/DPhil in a biomedical science related discipline. You will possess established expertise in data analysis
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hold a PhD (or be close to completion) in, for example, econometrics, statistics, or computer science, with experience of analysing large datasets. The post offers the opportunity to contribute to a
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work to generate high fidelity models of ice crystal icing shedding, verifying tools using a wealth of unique experimental validation data generated by researchers at the Oxford Thermofluids Institute